By the fastCRW team · Benchmarks/pricing verified 2026-05-18 · Verify independently before buying.
Disclosure: We build fastCRW, so this buyer's guide is vendor-authored — weight it accordingly. We have kept the places where other tools genuinely win explicit, and we publish the full benchmark distribution alongside every headline number, because a guide that hides the tail is not useful to you.
What makes a web data API "LLM-ready"
An LLM-ready web data API is not just a scraper with a JSON envelope. The phrase means the output drops into a retrieval or agent pipeline without a hand-built cleanup stage. Three properties decide whether an API earns the label:
- Clean markdown that preserves structure. Headings, lists, tables, and link text survive; navigation chrome, cookie banners, and footers are stripped. Markdown costs far fewer tokens than raw HTML and chunks predictably for embeddings.
- Structured JSON via schema. When you need fields, not prose — price, author, SKU, publish date — the API should accept a JSON schema and return typed values, not a wall of text you parse downstream.
- Freshness and search-then-scrape. Agents reasoning about the live web need a way to discover URLs (search) and fetch their content in the same loop, not a stale crawl from last week.
Tools that emit raw HTML or a brittle DOM tree are not LLM-ready in this sense; they are an upstream dependency you still have to finish. The differentiator is whether the markdown and JSON are accurate and complete, because garbage in means garbage RAG.
The buyer's criteria
If you are choosing an API to feed clean web data into RAG or agents, rank candidates on three measurable axes — in this order.
Extraction accuracy (recall on labeled data)
This is the criterion most buyer's guides skip because it is hard to measure, and it is the one that decides downstream quality. If the API silently drops half a page's content, your retriever never sees it. The only honest way to compare is recall against a labeled dataset, not a vendor's hand-picked demo URL.
Latency: median and the tail
A single "average latency" number hides the story. What matters is the median (your typical request) and the tail (p90/p99), because the slow tail is what times out an agent mid-reasoning. Insist on the full split; treat any vendor that quotes one mean as withholding information.
Pricing model and self-host option
Per-page flat pricing is predictable; per-GB or per-feature metering balloons unpredictably at agent scale. And an API you can self-host gives you a hard worst-case cost ceiling — the server bill — that a hosted-only model structurally cannot offer.
LLM-ready web data APIs compared
The market splits into three rough camps. Here is how the main options map, with the trade-off each one asks you to accept.
| Tool | Camp | LLM-ready output | Self-host | Trade-off to accept |
|---|---|---|---|---|
| fastCRW | Open-core scrape + crawl + search | Markdown + JSON schema + search | Yes (AGPL-3.0) | Lowest p90 in fast mode of the three benched; built-in anti-bot + proxy rotation |
| Firecrawl | Managed AI web-data API | Markdown + JSON + agentic endpoints | AGPL, heavy stack | Cloud-only for full feature set; extract often billed separately |
| Tavily / Exa | Search-first for agents | Search results + snippets | No | Search-native, not a full-page scrape/crawl engine |
| Jina Reader (r.jina.ai) | URL-to-markdown | Thin markdown | No (token-metered) | One URL at a time; no crawl, no schema extraction |
If you want a deeper field comparison of full scrape engines, our best web scraping APIs roundup and best web scraping API for 2026 guide go tool by tool. This page is the LLM-readiness lens specifically.
fastCRW: accuracy-led, with the full latency split
fastCRW is an open-core, Firecrawl-compatible engine — a single static Rust binary, AGPL-3.0, drop-in after a base-URL swap. On the criteria above, here is exactly where it lands.
Highest truth-recall of the three tools tested
On Firecrawl's own public scrape-content-dataset-v1 — 819 of its 1,000 URLs carry labeled ground truth — fastCRW posted the highest truth-recall of the three tools tested: 63.74% of 819 labeled URLs, versus Crawl4AI 59.95% and Firecrawl 56.04% (diagnose_3way.py, 2026-05-08). For an LLM-ready API, recall is the headline criterion, because content the scraper drops is content your retriever can never surface.
p50 beats Firecrawl; p90 lowest of the three in fast mode
On latency, fastCRW's median is p50 1914 ms, beating Firecrawl's 2305 ms and effectively tied with Crawl4AI (1916 ms). In fast mode, fastCRW's p90 is 4348 ms — the lowest of the three (Crawl4AI 4754 ms, Firecrawl 6937 ms). Scrape-success was ~92% of reachable URLs with 0 thrown errors across 3,000 requests in the same run. Always read the full p50/p90/p99 split, never a single mean.
1 credit = 1 page; self-host for $0
Pricing is flat: any scrape is 1 credit regardless of renderer (http, lightpanda, or chrome — no JS-rendering surcharge), and JSON-schema extraction is that 1-credit scrape plus the LLM token cost, billed as usage-metered LLM credits — the web leg is a flat credit on every renderer, but the LLM usage scales with page size and token usage. Self-hosting the AGPL-3.0 engine costs $0 per 1,000 scrapes; you pay only for your own server, versus roughly $0.83–5.33 per 1,000 on Firecrawl's hosted tiers (competitor-prices.lock.md, verified 2026-05-18). See live tiers on /pricing rather than trusting a hard-coded table.
Matching the tool to the job
Most workloads that need scrape, crawl, and search in one credit pool are well served by the numbers above — including a Research API (/v2/search/research/papers) for arXiv/OpenAlex/Semantic Scholar fan-out, and structured JSON extraction across up to 50 URLs in a single /v1/extract call. A narrower niche exists for tools built around one job: if your workload is search-only, with no page-fetch step at all, a search-native API like Tavily or Exa is a lighter-weight fit — though fastCRW's own /v1/search already covers search plus answer synthesis in the same wallet as scrape/crawl/map, and beat Firecrawl and Tavily on latency in our 100-query benchmark (880 ms average, 73/100 wins). LLM extraction runs on fastCRW's managed LLM, the same managed model behind the /v1/search answer path, and is available on paid plans only.
Choosing your web data API
Map the choice to the job, not to a feature checklist.
| Your job | What to optimize for | Lean toward |
|---|---|---|
| RAG corpus building | Recall + whole-site crawl | fastCRW (highest recall, /v1/crawl + /v1/map) |
| Live agent context | Search + scrape in one loop, low median latency | fastCRW search or a search-native API |
| Tail-latency-critical inline calls | Tight p90/p99 | fastCRW fast mode (lowest p90 of the three tested) |
| Hardened anti-bot targets | Residential proxies, stealth | fastCRW's built-in anti-bot + proxy rotation; a dedicated vendor only for the most extreme targets |
| Privacy / regulated data | Data never leaves your infra | fastCRW self-host |
| Single-URL markdown, occasional use | Simplicity | Jina Reader or fastCRW /v1/scrape |
For the output format itself — when markdown wins and when you should reach for JSON-schema extraction — see our walkthrough on LLM-ready markdown extraction. The short version: markdown for retrieval and chunking, JSON for typed fields you will query.
How to run a fair trial
Because fastCRW is Firecrawl-compatible, you do not have to decide on argument. Point the official Firecrawl SDK at a fastCRW base URL, run the same pipeline against both for a week on identical URLs, and capture four numbers identically: content-parity rate on a labeled sample, p50 and p90 latency, error mix, and projected monthly bill including any separate extraction subscription. Let the numbers arbitrate. If the tail matters more than recall for your traffic, the data will say so; if recall and median win, you have already migrated.
Sources
- fastCRW scrape benchmark of record:
bench/server-runs/RESULT_3WAY_1000_FULL.md(diagnose_3way.py, Firecrawl public dataset, 819 labeled URLs, 2026-05-08) - Competitor pricing:
marketing/competitor-prices.lock.md(verified 2026-05-18) · firecrawl.dev/pricing - fastCRW repo and pricing: github.com/us/crw · fastcrw.com
Related: Best web scraping APIs · LLM-ready markdown extraction · Best web scraping API 2026